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Archive for the ‘Developmental Neuroscience’ Category

Trends in neurodevelopment are, at least to me, a bit counterintuitive. It is surprising that there would be the most synaptic connections in humans at ~ 8 months after birth rather than, say, 18 years. But following the logic of synaptic pruning, this is the world we live in.

Using light and electron microscopy, a new study sheds some light on these processes. The authors provide quantitative measurements of the trade-off to large numbers of synapses in newborn mice, which is that each individual axon and synapse is smaller.

They study the motor axons of neuromuscular junctions, but presumably the same patterns of redistribution generalize to elsewhere in the nervous system. Some of their findings:

  • At birth, the main branch of the motor axons entering muscles had an average diameter of 1.48 ±0.03 μm, compared to 4.08 ±0.07 μm at 2 weeks old
  • In the cleidomastoid, at birth each motor axon innervated an average of 221 ±6.1 different muscle fibers, compared to 18.8 ±3.0 at 2 weeks old
  • At embryonic day 18, each terminal axon branch covered an average of 14.2 ±11.4% of the muscle’s acetylcholine receptors, compared to ~100% by single axons in adults

These results and others in the paper show that although there are fewer total synapses in later stages of development, each axon/synapse is bigger and more specific.

Reference

Tapia JC, et al. Pervasive synaptic branch removal in the Mammalian neuromuscular system at birth. 2012 Neuron, PMID: 22681687.

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The relationship between the anterior insula and the posterior parietal cortex is believed to play a role in linking the default mode network. In their Feb ’12 study, Supekar et al. used resting-state fMRI as well as DTI to hone in on the multimodal connection between these two brain regions.

One of their findings is that the overall fiber density between these regions is about 1.5x greater in adults (mean age of ~20) than in children (mean age of ~8).

fiber density = number of fibers / unit area; red = right posterior parietal cortex, yellow = right anterior insula; doi:10.1371/journal.pcbi.1002374

The reconstructed nerve bundles in cyan are part of the superior longitudinal fasciculus. The age differences are relatively unsurprising because myelination is a well-regulated process depending upon many growth factors and transcriptional events, which surely encompasses many years.

They also found a dependency between fiber density (i.e., structural connectivity) and instantaneous correlations in activity (i.e., functional connectivity) between the anterior insula and posterior parietal cortex in adults but not children.

rAI = right anterior insula, rPPC = right posterior parietal cortex; they found a correlation in adults (r = 0.44) but not children (r = 0.02); doi:10.1371/journal.pcbi.1002374

The same group previously made a similar finding relating the posterior cingulate cortex and the medial prefrontal cortex. Thus it seems to be a trend that structure-function relationships become more abiding as development progresses.

This makes sense to me within an energy savings framework. It only seems worth it to spend valuable energy tuning the synaptic weights between regions when the structural connections are stable. Still, we’ll have to wait for more data to judge this hypothesis.

Reference

Supekar K, Menon V (2012) Developmental Maturation of Dynamic Causal Control Signals in Higher-Order Cognition: A Neurocognitive Network Model. PLoS Comput Biol 8(2): e1002374. doi:10.1371/journal.pcbi.1002374

Supekar K, et al. 2010 Development of functional and structural connectivity within the default mode network in young children. Neuroimage. http://dx.doi.org/10.1016/j.neuroimage.2010.04.009

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Humans seem to have developed dedicated systems for detecting the prototypical gait of moving animals. One paradigm for operationalizing this ability is a point light display, which simulates animals moving in the dark with just a few lights on their joints.

We are able to classify these sparse moving points as biological motion and can often even make inferences about the characteristics of the moving agent. See for yourself in this 31 s video:

Previous studies have indicated that toddlers with autism have deficits in perceiving biological motion. This is not surprising, because social information is embedded within the stimuli.

Kaiser et al took this further by using this point light display paradigm and fMRI on 1) children with ASD, 2) siblings of children with ASD, and 3) control children.

They looked for regions differentially activated between biological light displays and scrambled light displays. They then compared the degree of differential neural activity between groups.

Brain regions were classified as having 1) less differential activation in ASD children in biological conditions as compared to siblings and controls (orange below), 2) less differential activation in ASD children and siblings as compared to controls (yellow), 3) enhanced differential activation in siblings (green), or 4) no statistically significant difference in differential activation between groups (uncolored).

top = sagittal slice; middle = coronal; bottom = axial; doi: 10.1073/pnas.1010412107

Their approach helps tease out the neural circuits underlying why some individuals with genetic risk factors don’t develop ASD. The two main brain regions they implicated were the vmPFC (of emotional decision making fame) and the right posterior STS. Could we imagine some study attempting to stimulate these regions in a model of ASD to mimic the development of compensatory mechanisms?

Reference
Kaiser M, et al. 2010 Neural signatures of autism. PNAS doi:10.1073/pnas.1010412107.

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When neurons adhere (independently) to a 2-d substrate, they often migrate in a similar direction. One mechanism for this is the entanglement of their outgrowing neurites, via cell adhesion molecules, in a process sometimes called fasciculation.

Initially, these fasciculated neurites should be in tensile equilibrium. However, during development and migration the cell with the stronger tensile force will tend to pull the other neurite (and thus its associated neuron) closer.

Over time, this tendency would cause neurons to cluster together. An interesting new study from Sun et al demonstrates this clustering effect nicely.

When cells are plated at a relatively low density, they tend to form multiple clusters. For example, see the schematic below showing the migration of hippocampal neurons plated on a circular substrate over 24 hours:

"the blue arrow begins from the original position of the cells at 0 hr, and ends at the final location at 24 hr"; transparent pink shapes = clusters at the end; scale bar = 200 µm; doi:10.1371/journal.pone.0028156.g003 part c

When the cells are relatively more dense, they will typically form one big mega-cluster.  As an example, see this set of time-lapse images of hippocampal neurons grown over 12 days in vitro (DIV):

red arrows = clusters; scale bar = 200 µm; doi:10.1371/journal.pone.0028156.g003 part a

Their model predicts that a genetic or biochemical intervention which inhibits neurite fasciculation would reduce the clustering of neurons in this sort of system.

Reference

Sun Y, Huang Z, Yang K, Liu W, Xie Y, et al. (2011) Self-Organizing Circuit Assembly through Spatiotemporally Coordinated Neuronal Migration within Geometric Constraints. PLoS ONE 6(11): e28156. doi:10.1371/journal.pone.0028156

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The human brain transcriptome database contains genome-wide RNA expression data in various regions of 57 post-mortem human brains collected from “clinically unremarkable” donors across the lifespan.

It is very easy to search for the expression patterns of a particular gene, and the output is easily interpreted. As an example, here are the results of a search for DRD2 across different brain regions:

NCX: neocortex; HIP = hippocampus; AMY = amygdala; STR = striatum; MD = mediodorsal nucleus thalamus; CBC = cerebellar cortex; accessed from http://hbatlas.org/pages/hbtd

The expression of DRD2 increases in the striatum during development and remains high throughout adulthood, consistent with its associations with striatal function.

Reference

Kang HJ, et al. 2011 Spatio-temporal transcriptome of the human brain. doi:10.1038/nature10523.

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Brennand et al took an interesting approach to this question in their recent article. They took some non-neuronal cells from four patients with schizophrenia (and six controls), reprogrammed them into stem cells, induced them to become neural progenitor cells (mostly glutamatergic), and, finally, cultured them in vitro.

The researchers then used trans-synaptic viral tracing to visualize the connections between cultured neurons in the schizophrenia patients and healthy controls with fluorescent proteins.

To me their major finding was that the cultured cells of patients with schizophrenia had fewer connections. That is, the ratio of cells initially infected with the virus to cells secondarily infected through a synaptic connection was lower in the schizophrenia patients. Eyeing fig 2b, it looks like this ratio is ~ 1.2 +/- 0.1 in the cells from healthy patients, while it is ~ 0.55 +/- 0.05 in the cells from patients with schizophrenia. They show some beautiful representative images of this, which I at first thought must be false colored. But no, they’re real. Wow.

The degree to which schizophrenia manifests as a “connectopathy” is a highly contentious issue, but this study adds to the suggestion that neural connectivity does play at least some sort of a role in the etiology of the disease.

(Also blogged at Neuroskeptic.)

Reference

Brennand KJ, Simone A, Jou J, Gelboin-Burkhart C, Tran N, Sangar S, Li Y, Mu Y, Chen G, Yu D, McCarthy S, Sebat J, & Gage FH (2011). Modelling schizophrenia using human induced pluripotent stem cells. Nature. doi:10.1038/nature09915 PMID: 21490598

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Leuner and Gould’s article shows this to be the case. They compared brain tissue slices of mothers post-birth days 20-25 to those of control (virgin) rats. Specifically, the researchers looked at pyramidal neurons in the 1) anterior cingulate area of the medial prefrontal cortex, 2) orbitofrontal cortex, and 3) CA1 / dendate gyrus of the hippocampus. They then used Golgi-Cox staining and light microscopy to examine the differences in the number of dendritic spines between the groups. Here are their main anatomical results:

a = Golgi-stained neurons in the medial prefrontal cortex, arrows = dendritic spines, * = significant diffs via t-tests; doi: 10.1523/ JNEUROSCI.3388-10.2010

They also found that cognitive ability was better in the mothers in an attention-based set-shifting task, indicative of improved medial prefrontal cortex performance. So it seems plausible that the dendritic spine density changes are related to the improvements in cognitive ability.

As the authors point out in the discussion, living in an enriched environment could also be a factor here. That is, control rats were housed individually, so one could make the argument that the social contact is what is driving these changes, as opposed to parenting per se. Further research might attempt to differentiate these, and to determine what types of dendritic remodeling are specific to parenting.

Reference

Leuner B, et al. 2010 Dendritic Growth in Medial Prefrontal Cortex and Cognitive Flexibility Are Enhanced during the Postpartum Period. J Neuro, doi: 10.1523/​JNEUROSCI.3388-10.2010 .

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